I am trying to find a way to analyze a "simple" mixed model with two
levels of a treatment, a random blocking factor, and (wait for it)
negative binomial count distributions as the response variable. As far as
I can tell, the currently available R offerings (glmmGibbs, glmmPQL in
MASS, and Jim Lindsey's glmm code) aren't quite up to this. From what I
have read (e.g. http://www.stat.ufl.edu/~jhobert/papers/nb7.ps), SAS PROC
NLMIXED seems like the best way to go. I can go do that if I have to, but
I'd prefer to do it in R if that seems feasible.
Some playing around with simulations that mimic the structure of the
data and using various combinations of glmmPQL, glm.nb, quasipoisson
family, etc., suggest that I really can't quite get there with glmmPQL
(which is what I've mostly tried); I get an estimate, but the confidence
intervals on the block variance are astronomical (perhaps intervals.lme
fails?)
Any suggestions?
(I can provide more information/examples if anyone's interested.)
Ben Bolker
--
318 Carr Hall bolker at zoo.ufl.edu
Zoology Department, University of Florida http://www.zoo.ufl.edu/bolker
Box 118525 (ph) 352-392-5697
Gainesville, FL 32611-8525 (fax) 352-392-3704
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